Investigating Configurational and Active Centralities: the Example of Metropolitan Copenhagen Andrakakou Maria Supervisor: Carst

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Investigating Configurational and Active Centralities: the Example of Metropolitan Copenhagen Andrakakou Maria Supervisor: Carst Investigating configurational and active centralities: the example of metropolitan Copenhagen Andrakakou Maria Supervisor: Carsten Kessler Copenhagen, 2020 Cover is a snapshot from: Perpetual Motion. 2019. [video] Directed by M. Cooper and N. Cobby. Acknowledgements This master thesis offered me the opportunity to combine coding and spatial analysis and has improved my knowledge on Copenhagen’s urban structure. However, that would not have been achieved without the support of certain persons. Initially, I would like to thank my thesis supervisor and professor, during my studies at Aalborg University Copenhagen, Carsten Kessler for providing his guidance and support when it was a necessity. His contribution was beneficial during the implementation phase of this research as well as during the development of this report. Additionally, many thanks to Yannis Paraskevopoulos for inspiring me with this idea and for motivating me. His main suggestion was to develop an open source workflow in R for processing space syntax calculations which would assist many researchers with similar interests. 2 Summary Contemporary metropolitan cities tend to be less dense and more car-oriented than in the past. Centralities existing within cities help determine how public space is perceived and utilized in everyday life. Sustainable mobility, social sustainability, and spatial justice can be examined by investigating centralities within the urban form. Following this rationale, the study focuses on pedestrian flows in Copenhagen’s metropolitan area and provides a methodological framework for visualizing and analyzing them. More precisely, the current master thesis study aims to investigate configurational centralities created by the road network based on space syntax analysis and active centralities of land-use patterns with a more geographical approach; finally, it analyzes their relation. Kernel Density Correlation is the tool applied to examine their relation. Furthermore, land-use mix is calculated for certain municipalities in metropolitan Copenhagen based on the Diversity Index. The combination of these approaches allows locating areas that have potential for improvement of their land-use distribution or their road network infrastructure. Land-use mix calculated based on Diversity Index remains at a descent level for all municipalities. However, land-use mix has potential for improvement in particular municipalities. Areas with lower land-use mix are in proximity to areas with higher land-use mix and a good example of that is central Copenhagen’s case and its neighboring municipalities. Configurational centralities highlight two cases receiving intermunicipal thus car- oriented flows; Roskilde and Copenhagen municipalities. Centralities in Albertslund, Greve and Ishøj municipalities are designed for 5-minute walks as KDE for AC-400 highlights while centralities clustered around the axes of the Finger Plan are designed for 10-minute walks as KDE for AC-800 suggests. However, equally distributed centralities lack in northern municipalities as weel as in areas between the Finger Plan’s main axes. Active centralities, resulting from non-residential land-use KDE, are more intense in areas of Copenhagen and Roskilde city centre and in several other municipalities. Dispersed active centralities are closer to the edges of the study area and within areas located far from the main axes of Copenhagen’s Finger Plan. Kernel density correlation shows that configurational and geographical patterns have stronger correlation with local scales and less strong with the global scale. This verifies the hypothesis that configurational centralities are an important factor for shaping land-use (active) centralities. The results of correlations indicate that areas close to the city center and around the Finger Plan tend to be more central and friendlier for pedestrians and cyclists. Furthermore, centralities concentrated around Finger Plan΄s axes are decentralized, more clustered and more continuous showing that its transit- oriented purpose has succeeded. On the contrary, central areas far from the city center, especially in the Northern part, and areas between the axes of the Finger 3 Plan are more car-oriented since centralities are dispersed and located around highways or road segments designed for cars. Overall, the analysis indicates that there should be better organized for northern municipalities regarding their land-use distribution and road network. In addition, the Finger Plan is well established according to configurational and active centralities. A better land-use dataset and a further investigation including Angular Integration calculations as well as a combination with population census data are necessary tools for exploring the relations among municipalities and centralities. However, this study is a useful input for investigating the role of centralities within metropolitan Copenhagen and for highlighting areas in need of further research. 4 Contents 1. Introduction .............................................................................................................................. 7 2. Theoretical background ....................................................................................................... 8 2.1 Space syntax as a measure of configurational centrality ..................................... 8 2.2 Kernel Density Estimator as a measure of functional centrality ..................... 11 2.3 Study area ............................................................................................................................. 12 3. Methodology ........................................................................................................................... 15 4. Data manipulation ................................................................................................................ 17 4.1 Software and tools ............................................................................................................. 17 4.2 Data sources and description ........................................................................................ 17 4.3 Data preparation and process ....................................................................................... 19 4.4 Kernel Density Correlation ............................................................................................ 22 4.5 Spatial correlation at a municipality level ............................................................... 24 5. Results ....................................................................................................................................... 25 6. Discussion ................................................................................................................................ 37 7. Conclusion ................................................................................................................................ 38 8. Future development ............................................................................................................. 39 Bibliography..................................................................................................................................... 41 Appendix ........................................................................................................................................... 43 Glossary KDE: Kernel Density estimation KDC: Kernel Density Correlation AC: Angular Choice LU: Land-use DIV: Diversity index 5 Table of figures Figure 1: Finger plan 1947 (source: (https://planinfo.erhvervsstyrelsen.dk/) ..................................... 13 Figure 2: Copenhagen's Finger plan 2019 (source: https://planinfo.erhvervsstyrelsen.dk/) ......... 14 Figure 3: Methodology workflow ................................................................................................................................. 16 Figure 4: Code snippet example in python for downloading shops in Denmark with Overpass API ..................................................................................................................................................................................................... 18 Figure 5: Place Syntax Tool and creation of segment map ............................................................................... 19 Figure 6: Line density methodology provided by ESRI (source: https://pro.arcgis.com/en/pro- app/tool-reference/spatial-analyst/how-line-density-works.htm) ............................................................ 20 Figure 7: KDE raster layers creating the correlation table (source: Strano et al. 2007) ..................... 23 Figure 8: Example of the square 5x5 moving window ....................................................................................... 24 Figure 9: R snippet for local correlation using Rochette’s (2018) method ............................................... 25 Figure 10: Diversity index of land-use within municipalities ......................................................................... 27 Figure 11: Examples of Angular Choice calculations for space syntax analysis ...................................... 28 Figure 12: Angular choice calculations KDE for local and global scales ..................................................... 30 Figure 13: Land-use KDE ................................................................................................................................................. 31 Figure 14: KDE for angular choice values and 800m radius (left), KDE
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